Learning, artificial intelligence

R. S. Michalski, R. E. Stepp. Learning from Observation Ganceptual Clustering, in Machine Learning An Artificial Intelligence Approach, R.S. Michalski, f.G. CarboneU, T.M. Mitchell (Eds.), Morgan Kauffmatm, San Mateo, GA, 1983, pp. 331-363. 

Materials selection is as much an art as a rigorous science, and another computational approach to it, based on ideas of artificial intelligence, has been proposed by Arunachalam and Bhaskar (1999). They call their approach bounded rationality and exploit it to analyse the background to some notorious disasters based on material failure. We can always learn from failure as well as from success. 

Kodratoff, Y., and Michalski, R., eds., Machine Learning An Artificial Intelligence Approach. Vol. 3. Morgan Kaufinann, San Mateo, CA, 1990. 

Several techniques from statistics, such as partial least-squares regression, and from artificial intelligence, such as artificial neural networks have been used to learn empirical input/ output relationships. Two of the most significant disadvantages of these approaches are the following ... 

R. Forsyth and R. Rada, Machine Learning Applications in Expert Systems and Information Retrieval. Ellis Horwood Series in Artificial Intelligence. Ellis Horwood, Wiley, Chichester, 1986. 

The brain s remarkable ability to learn through a process of pattern recognition suggests that, if we wish to develop a software tool to detect patterns in scientific or, indeed, any other kind of data, the structure of the brain could be a productive starting point. This view led to the development of artificial neural networks (ANNs). The several methods that are gathered under the ANN umbrella constitute some of the most widely used applications of Artificial Intelligence in science. Typical areas in which ANNs are of value include ... 

The method is different in nature from the techniques discussed elsewhere in this book because, despite the fact that the cells that comprise a CA model evolve, they do not learn. The lack of a learning mechanism places the method at the boundaries of Artificial Intelligence, but the algorithm still has features in common with AI methods and offers an intriguing way to solve scientific problems. 

Lanzi, P.L., Stolzmann, W., and Wilson, S.W., (Eds.) Learning classifier systems From foundations to applications, Lecture Notes in Artificial Intelligence 1813, Springer, Berlin, 2000. 

He has written, edited, and provided invited chapters in a number of textbooks on the use of Artificial Intelligence in science and continues to run an active research group in the area. He has taught at universities in Canada, the United States, Ireland, and the United Kingdom, and is a board member of the CoLoS (Conceptual Learning of Science) International Consortium. 

An artificial intelligence program is a piece of computer software that can learn. 

Michalski, R. S. Stepp, R. E. "Learning from Observation Conceptual Clustering" In "Machine Learning An Artificial Intelligence Approach" Michalski, R. S. Carbonell, J. G. Mitchell, T. M., Eds. Tioga Press, 1983. 

Hurlbert AC and Poggio TA 1987 Learning a Color Algorithm from Examples. Technical Report A.I. Memo No. 909, Massachusetts Institute of Technology, Artificial Intelligence Laboratory. 

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